Papers
12
Total Citations
493
H-Index
7
About
Julian Nubert is a robotics researcher whose work spans autonomous navigation, state estimation, and safety-critical control systems — areas increasingly vital as robots move from controlled settings into unpredictable real-world environments. His most influential contribution, "Safe and Fast Tracking on a Robot Manipulator" (2020, 221 citations), elegantly bridges robust model predictive control with deep neural networks to simultaneously guarantee safety and high-speed performance — a long-standing tension in the field. His work on LiDAR-based localization has been equally impactful: "X-ICP" (2023, 111 citations) addresses a critical vulnerability in robotic perception by making point-cloud registration reliable even in geometrically degenerate environments, from featureless tunnels to snowy landscapes. Nubert has also advanced multi-sensor fusion for construction robotics, off-road traversability learning, and visual semantic navigation, demonstrating a rare breadth across both perception and planning. His 2025 survey on continuous-time state estimation methods reflects a growing role as a synthesizer of the field's foundations. With over 480 citations across a relatively compact publication record, Nubert's research consistently targets high-stakes autonomy challenges where reliability and robustness are non-negotiable — making his work essential reading for anyone building robots that must perform in the wild.
Research Focus
Key Achievements
Top Papers
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- 5ViPlanner: Visual Semantic Imperative Learning for Local Navigation26 citations · 2024
- 6Airborne Particle Classification in LiDAR Point Clouds Using Deep Learning23 citations · 2021
- 7Continuous-Time State Estimation Methods in Robotics: A Survey11 citations · 2025
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- 10Self-supervised Learning of LiDAR Odometry for Robotic Applications5 citations · 2021